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一种用于汽车行业的混合数据差分与压缩算法。

A Hybrid Data-Differencing and Compression Algorithm for the Automotive Industry.

作者信息

Belu Sabin, Coltuc Daniela

机构信息

Doctoral School of Electronics, Telecommunications and Information Technology, Politehnica University of Bucharest, 061071 Bucharest, Romania.

出版信息

Entropy (Basel). 2022 Apr 19;24(5):574. doi: 10.3390/e24050574.

Abstract

We propose an innovative delta-differencing algorithm that combines software-updating methods with LZ77 data compression. This software-updating method relates to server-side software that creates binary delta files and to client-side software that performs software-update installations. The proposed algorithm creates binary-differencing streams already compressed from an initial phase. We present a software-updating method suitable for OTA software updates and the method's basic strategies to achieve a better performance in terms of speed, compression ratio or a combination of both. A comparison with publicly available solutions is provided. Our test results show our method, Keops, can outperform an LZMA (Lempel-Ziv-Markov chain-algorithm) based binary differencing solution in terms of compression ratio in two cases by more than 3% while being two to five times faster in decompression. We also prove experimentally that the difference between Keops and other competing delta-creator software increases when larger history buffers are used. In one case, we achieve a three times better performance for a delta rate compared to other competing delta rates.

摘要

我们提出了一种创新的增量差分算法,该算法将软件更新方法与LZ77数据压缩相结合。这种软件更新方法涉及创建二进制增量文件的服务器端软件以及执行软件更新安装的客户端软件。所提出的算法从初始阶段就创建已压缩的二进制差分流。我们提出了一种适用于OTA软件更新的软件更新方法以及该方法在速度、压缩率或两者结合方面实现更好性能的基本策略。还提供了与公开可用解决方案的比较。我们的测试结果表明,我们的方法Keops在两种情况下的压缩率方面比基于LZMA(Lempel-Ziv-马尔可夫链算法)的二进制差分解决方案高出3%以上,同时解压速度快两到五倍。我们还通过实验证明,当使用更大的历史缓冲区时,Keops与其他竞争的增量创建软件之间的差异会增加。在一种情况下,与其他竞争的增量率相比,我们在增量率方面实现了三倍的更好性能。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7a3f/9140898/e24c2c816ec7/entropy-24-00574-g001.jpg

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